Alphabeta Math
DefinitionDefinition: Literature-sourcedProof: Not applicablePipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Paths, active paths, and d-separation in a finite DAG

Definition

A finite directed acyclic graph (DAG) is a finite vertex set V with an arrow set E⊆V×V and no directed cycle. A path from u to v is a finite sequence of distinct vertices beginning at u and ending at v in which each consecutive pair is joined by an arrow in either direction; the arrows are not required to point along the sequence. An internal vertex of a path is a collider on that path when the two arrows on the path both point into it, and is a noncollider otherwise. A vertex w is a descendant of v if there is a directed path from v to w; for this definition De⁡+(v) includes v itself as well as its descendants.

For pairwise disjoint vertex sets X,Y,C⊆V, a path joining X to Y is active given C when every internal noncollider is outside C and every internal collider has at least one member of De⁡+(v) in C. The set C d-separates X and Y in G when no path joining X to Y is active given C; write X⊥dY∣C in G. The empty set of vertices is allowed for any of X,Y,C; in particular d-separation is vacuous if either endpoint set is empty.

For T⊆V, GT‾ is obtained by deleting every arrow whose tail lies in T, and GT‾ by deleting every arrow whose head lies in T. These are graph operations only; they do not by themselves specify a probability law under intervention.

Used by

Dependency tree · 0 levels

Nothing. This result depends on no other item in the library.

Sources